Abstract Knowledge graphs (KGs) model entities or concepts and their relations in a
structural manner. The incompleteness has turned out to be the main challenge that hinders …

A wide range of real-world applications is characterized by their symbolic nature,
necessitating a strong capability for symbolic reasoning. This paper investigates the …

Communication via natural language is a key aspect of machine intelligence, and it requires
computational models to learn and reason about world concepts, with varying levels of …

In this work, we study reinforcement learning (RL) in solving text-based games. We address
the challenge of combinatorial action space, by proposing a confidence-based self-imitation …

Text-based games (TGs) are exciting testbeds for develo** deep reinforcement learning
techniques due to their partially observed environments and large action spaces. In these …

Text-based games offer a challenging test bed to evaluate virtual agents at language
understanding, multi-step problem-solving, and common-sense reasoning. However, speed …

Text-based games (TGs) are language-based interactive environments for reinforcement
learning. While language models (LMs) and knowledge graphs (KGs) are commonly used …